WISE OWL EXERCISES
POWER BI EXERCISES
- PowerPivot data models (7)
- Pivot tables using PowerPivot (2)
- Using Excel tables (3)
- Using other data sources (1)
- Transforming data (Power Query) (7)
- Calculated columns (7)
- Measures (2)
- The CALCULATE function (15)
- More advanced DAX functions (5)
- Calendars (1)
- Date functions (10)
- Hierarchies (2)
- KPIs (5)
- Power View (4)
- Power BI Desktop overview (3)
- Power BI Desktop maps (1)
PowerPivot | Calculated columns exercise | Using the SWITCH Function to Sum by Age Band
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The aim of this exercise is to divide staff up into 3 bands as follows:
|Year of date of birth||Description|
|Up to and including 1960||Old|
|Up to and including 1985||Middle-aged|
You should then use this to get a pivot table showing the average quantity of goods in each transaction by age category, as well as the number of transactions:
There isn't much difference, making one wonder whether this is genuine data, or just randomly generated numbers. Surely Wise Owl wouldn't stoop so low?
To start, if you haven't already done so run the script in the above folder to generate the MAM database (not for commercial use or copying).
Now import the tblStaff, tblPos and tblTransaction tables (giving them friendly names), and create a relationship between the Staff and Pos tables.
In the staff table, create two new calculated columns:
|Column name||What it should contain|
|BirthYear||The year in which this person was born.|
|AgeBand||A verdict on the person's age, using the table above.|
Use either a nested IF function to get the age band, or else (better) the SWITCH function.
Use this information to derive the pivot table at the start of this exercise.
Save your workbook as Feeling old, then close it down.